MaveScore explained
A transparent stock score built for research review not advice.
MaveScore turns several research inputs into a 0-100 model output so investors can inspect the drivers, compare companies, and decide what still needs manual review.
Research support only. Mavefund does not provide personalized financial advice, and model output is not a guarantee of future performance.
Research workflow
Move from market change to company-level review.
Mavefund is organized around the questions investors ask before adding a stock to a watchlist.
Quality and profitability
Review earnings quality, margin durability, return on equity, and balance-sheet strength.
Open Microsoft researchGrowth and valuation
Compare growth context with valuation ratios so high scores are not viewed without price discipline.
Open Amazon researchTechnical and news context
Use price history, market signals, and headline impact as supporting research context.
Open headline analysisPopular company research
Start with high-interest stock analysis pages.
These priority pages are open for research discovery and linked from Mavefund's priority sitemap. Use them to inspect company fundamentals, MaveScore context, and risk prompts.
Risk review
What to check before acting outside Mavefund.
Signals are starting points for research. Keep risk, source freshness, and model limits visible before making any decision.
- A high score can still carry valuation, event, liquidity, or sector risk.
- A low score can reflect missing or stale data that deserves source review.
- A model output should be checked against filings, current prices, and the user's own constraints.
Next step
Use Mavefund as a daily research assistant.
Read the current market brief, review stock pages, and keep risk checks close to every model signal.